{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AB155B13302D459CBAAABA327849B76D",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "# 说明\n",
    "特征工程部分照搬社区的baseline未作改动，仅仅进行了模型的部分参数调整和基于stacking的模型融合。\n",
    "另外，由于是输出概率，后续按照回归去做，故删除了不平衡样本的处理，一开始当成分类去做，最高只能到0.8+，按照回归轻松0.9+\n",
    "参数仅做了简单的调整，非最优，线下0.9361018703876826，线上验证0.93536922"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C325760DBE164B398588C602C8C376BC",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "# 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "id": "0A028E17E34B4CFEABE9DFF956D89741",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "id": "001F87A7D41644E4BA2EA25513B0D1B3",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "train=pd.read_csv(r'/home/mw/input/data9803/train_set.csv')\n",
    "test=pd.read_csv('/home/mw/input/data9803/test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "id": "B8C543716B964973872549E7B4DFDFAA",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "data = pd.concat([train.drop(['y'],axis=1),test],axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "id": "D7CE94A95A2544AE808C26A4B1901BA7",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job :   ['management' 'technician' 'admin.' 'services' 'retired' 'student'\n",
      " 'blue-collar' 'unknown' 'entrepreneur' 'housemaid' 'self-employed'\n",
      " 'unemployed']\n",
      "marital :   ['married' 'divorced' 'single']\n",
      "education :   ['tertiary' 'primary' 'secondary' 'unknown']\n",
      "default :   ['no' 'yes']\n",
      "housing :   ['yes' 'no']\n",
      "loan :   ['no' 'yes']\n",
      "contact :   ['unknown' 'cellular' 'telephone']\n",
      "month :   ['may' 'apr' 'jul' 'jun' 'nov' 'aug' 'jan' 'feb' 'dec' 'oct' 'sep' 'mar']\n",
      "poutcome :   ['unknown' 'other' 'failure' 'success']\n"
     ]
    }
   ],
   "source": [
    "# 对object型数据查看unique\n",
    "str_features = []\n",
    "num_features=[]\n",
    "for col in train.columns:\n",
    "    if train[col].dtype=='object':\n",
    "        str_features.append(col)\n",
    "        print(col,':  ',train[col].unique())\n",
    "    if train[col].dtype=='int64' and col not in ['ID','y']:\n",
    "        num_features.append(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "29CB9E135078402DBE2F479858631C1D",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "id": "928046C510F54165A50D2FFBEFB4A5B5",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency       # 数值型特征检验，检验特征与标签的关系\n",
    "from scipy.stats import f_oneway,ttest_ind     # 分类型特征检验，检验特征与标签的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "id": "B5098300108B4CB784E1E1D8A1A7AF40",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "#----------数据集处理--------------#\n",
    "from sklearn.model_selection import train_test_split        # 划分训练集和验证集\n",
    "from sklearn.model_selection import KFold,StratifiedKFold   # k折交叉\n",
    "from imblearn.combine import SMOTETomek,SMOTEENN            # 综合采样\n",
    "from imblearn.over_sampling import SMOTE                    # 过采样\n",
    "from imblearn.under_sampling import RandomUnderSampler      # 欠采样\n",
    "\n",
    "#----------数据处理--------------#\n",
    "from sklearn.preprocessing import StandardScaler # 标准化\n",
    "from sklearn.preprocessing import OneHotEncoder  # 热独编码\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AB4FDF679736489B86708802E2B4E05B",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "## 特征处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B76FEED533744B93AD63ED6825716E5C",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "**连续变量即数值化数据做标准化处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "id": "DD0BE0C3EF0D4755A0F255565CA064B3",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# 异常值处理\n",
    "def outlier_processing(dfx):\n",
    "    df = dfx.copy()\n",
    "    q1 = df.quantile(q=0.25)\n",
    "    q3 = df.quantile(q=0.75)\n",
    "    iqr = q3 - q1\n",
    "    Umin = q1 - 1.5*iqr\n",
    "    Umax = q3 + 1.5*iqr \n",
    "    df[df>Umax] = df[df<=Umax].max()\n",
    "    df[df<Umin] = df[df>=Umin].min()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "id": "353D496D273C4456A8EF7C663D1C81C1",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "train['age']=outlier_processing(train['age'])\n",
    "train['day']=outlier_processing(train['day'])\n",
    "train['duration']=outlier_processing(train['duration'])\n",
    "train['campaign']=outlier_processing(train['campaign'])\n",
    "\n",
    "\n",
    "test['age']=outlier_processing(test['age'])\n",
    "test['day']=outlier_processing(test['day'])\n",
    "test['duration']=outlier_processing(test['duration'])\n",
    "test['campaign']=outlier_processing(test['campaign'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "605C7188AA404524AA2B935E363D1E6C",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.859502</td>\n",
       "      <td>1357.555082</td>\n",
       "      <td>15.835289</td>\n",
       "      <td>234.235138</td>\n",
       "      <td>2.391437</td>\n",
       "      <td>40.248766</td>\n",
       "      <td>0.591737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.387365</td>\n",
       "      <td>2999.822811</td>\n",
       "      <td>8.319480</td>\n",
       "      <td>175.395559</td>\n",
       "      <td>1.599851</td>\n",
       "      <td>100.213541</td>\n",
       "      <td>2.568313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>638.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  25317.000000   25317.000000  25317.000000  25317.000000  25317.000000   \n",
       "mean      40.859502    1357.555082     15.835289    234.235138      2.391437   \n",
       "std       10.387365    2999.822811      8.319480    175.395559      1.599851   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      73.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    181.000000      2.000000   \n",
       "75%       48.000000    1435.000000     21.000000    317.000000      3.000000   \n",
       "max       70.000000  102127.000000     31.000000    638.000000      6.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  25317.000000  25317.000000  \n",
       "mean      40.248766      0.591737  \n",
       "std      100.213541      2.568313  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      854.000000    275.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[num_features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FA30A072639C418283A5F9FF7BB70A5A",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "**分类变量做编码处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "id": "B843C59639844E1A872AECAFE7926878",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "dummy_train=train.join(pd.get_dummies(train[str_features])).drop(str_features,axis=1).drop(['ID','y'],axis=1)\n",
    "dummy_test=test.join(pd.get_dummies(test[str_features])).drop(str_features,axis=1).drop(['ID'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F2EA03BA7FAA45FF85F2CEDA21AB2F25",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "## 统计检验与特征筛选 \n",
    "\n",
    "\n",
    "**连续变量-连续变量  相关分析**\n",
    "\n",
    "**连续变量-分类变量  T检验/方差分析**\n",
    "\n",
    "**分类变量-分类变量  卡方检验**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "500456AC13CA4815858E06D09EE17A9E",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "**对类别标签（离散变量）用卡方检验分析重要性**\n",
    "\n",
    "卡方检验认为显著水平大于95%是差异性显著的，这里即看p值是否是p>0.05，若p>0.05，则说明特征不会呈现差异性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "40798A95334C4A85812FCB8A3CE6DD75",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job 卡方检验p值: 0.0000\n",
      "marital 卡方检验p值: 0.0000\n",
      "education 卡方检验p值: 0.0000\n",
      "default 卡方检验p值: 0.0001\n",
      "housing 卡方检验p值: 0.0000\n",
      "loan 卡方检验p值: 0.0000\n",
      "contact 卡方检验p值: 0.0000\n",
      "month 卡方检验p值: 0.0000\n",
      "poutcome 卡方检验p值: 0.0000\n"
     ]
    }
   ],
   "source": [
    "for col in str_features:\n",
    "    obs=pd.crosstab(train['y'],\n",
    "                    train[col],\n",
    "                    rownames=['y'],\n",
    "                    colnames=[col])\n",
    "    chi2, p, dof, expect = chi2_contingency(obs)\n",
    "    print(\"{} 卡方检验p值: {:.4f}\".format(col,p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "30A24D4C89AF4BDAA0115FDF8F7C12E5",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "**对连续变量做方差分析进行特征筛选**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "43185B05C4AB48628437C4224F86F348",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores_: [  13.38856992   84.16396612   25.76507245 4405.56959938  193.97418155\n",
      "  296.33099313  199.09942912]\n",
      "pvalues_: [2.53676251e-04 4.89124305e-20 3.88332900e-07 0.00000000e+00\n",
      " 6.26768275e-44 4.93591331e-66 4.86613654e-45]\n",
      "selected index: [0 1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest,f_classif\n",
    "\n",
    "f,p=f_classif(train[num_features],train['y'])\n",
    "k = f.shape[0] - (p > 0.05).sum()\n",
    "selector = SelectKBest(f_classif, k=k)\n",
    "selector.fit(train[num_features],train['y'])\n",
    "\n",
    "print('scores_:',selector.scores_)\n",
    "print('pvalues_:',selector.pvalues_)\n",
    "print('selected index:',selector.get_support(True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "id": "5AC3B3DFF3134BDC83E167086A48540B",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# 标准化，返回值为标准化后的数据\n",
    "standardScaler=StandardScaler()\n",
    "ss=standardScaler.fit(dummy_train.loc[:,num_features])\n",
    "dummy_train.loc[:,num_features]=ss.transform(dummy_train.loc[:,num_features])\n",
    "dummy_test.loc[:,num_features]=ss.transform(dummy_test.loc[:,num_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "id": "EEEA48883D2E4FA58FB392991E531C86",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "X=dummy_train\n",
    "y=train['y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A16ACA2680A243C0880DE1F19CFB6AAD",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "**因为后续是进行回归而非分类，个人认为没有必要进行不平衡处理，故此部分就注释掉了**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "id": "AFE7C8F818B148178A42245BBF4C8878",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# X_train,X_valid,y_train,y_valid=train_test_split(X,y,test_size=0.2,random_state=2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "id": "046EFE395ED34BFCAD11F2721BBE40FD",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "# smote_tomek = SMOTETomek(random_state=2020)\n",
    "# X_resampled, y_resampled = smote_tomek.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "13F7B7216054465D9437CEB872026BC3",
    "jupyter": {},
    "mdEditEnable": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "# 数据建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "0E1FC19C7DF74042B6F1A9F5527AA4AE",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py:13: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "#----------建模工具--------------#\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import KFold,RepeatedKFold\n",
    "import lightgbm as lgb\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "from catboost import CatBoostRegressor, Pool\n",
    "from lightgbm import LGBMRegressor\n",
    "#----------模型评估工具----------#\n",
    "from sklearn.metrics import confusion_matrix # 混淆矩阵\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import recall_score,f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D59F8B42DDB34650B81C23D56AE0BB43",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "## 模型建立和参数调整"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6C0DDA8DA53482ABA9E2EE753DB105D",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于GridSearchCV的随机森林参数调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "4451159F5E62465CB52012889AD801B2",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n",
      "{'max_features': 11, 'min_samples_leaf': 1, 'n_estimators': 1700}\n"
     ]
    }
   ],
   "source": [
    "# 随机森林\n",
    "# param = {'n_estimators':[1500,1700,2000],\n",
    "#          'max_features':[7,11,15]\n",
    "#         }\n",
    "# gs = GridSearchCV(estimator=RandomForestRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1, verbose=10) \n",
    "# gs.fit(X_resampled,y_resampled)\n",
    "# print(gs.best_params_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "821CD114AA87471B9188366261086B72",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于五折交叉验证的随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "46ED98A1821C450E85F2338B49A197C6",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.929373220326099\n"
     ]
    }
   ],
   "source": [
    "n_fold = 5\n",
    "folds = KFold(n_splits=n_fold, shuffle=True, random_state=2022)\n",
    "oof_rf = np.zeros(len(X))\n",
    "prediction_rf = np.zeros(len(dummy_test))\n",
    "for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n",
    "    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n",
    "    y_train, y_valid = y[train_index], y[valid_index]\n",
    "#     smote_tomek = SMOTETomek(random_state=2022)\n",
    "#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n",
    "    model_rf = RandomForestRegressor(max_features=11,min_samples_leaf=1,n_estimators=1700,random_state=2022).fit(X_train,y_train)\n",
    "    y_pred_valid = model_rf.predict(X_valid)\n",
    "    y_pred = model_rf.predict(dummy_test)\n",
    "    oof_rf[valid_index] = y_pred_valid.reshape(-1, )\n",
    "    prediction_rf += y_pred\n",
    "prediction_rf /= n_fold \n",
    "print(roc_auc_score(y, oof_rf))\n",
    "#0.929373220326099"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A03808ADDE7A4AC4AEFDC87F09A5A017",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于GridSearchCV的XGB参数调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "A26971BEEF684860B739522723029593",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n",
      "{'colsample_bytree': 0.6, 'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 800, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# param = {'max_depth': [3],\n",
    "#          'learning_rate': [0.01],\n",
    "#         'subsample':[0.8],\n",
    "#         'colsample_bytree':[0.6],\n",
    "#          'n_estimators': [8000]\n",
    "\n",
    "#         }\n",
    "# gs = GridSearchCV(estimator=XGBRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1, verbose=10) \n",
    "# gs.fit(X,y)\n",
    "# print(gs.best_params_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7AF59354E891478D8154F963C5EDE251",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于五折交叉验证的XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "id": "C0C60825A129420F8F4C983DEC926AC6",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.86541\n",
      "[1]\tvalidation_0-auc:0.88478\n",
      "[2]\tvalidation_0-auc:0.89516\n",
      "[3]\tvalidation_0-auc:0.90166\n",
      "[4]\tvalidation_0-auc:0.90600\n",
      "[5]\tvalidation_0-auc:0.90680\n",
      "[6]\tvalidation_0-auc:0.90747\n",
      "[7]\tvalidation_0-auc:0.90969\n",
      "[8]\tvalidation_0-auc:0.90979\n",
      "[9]\tvalidation_0-auc:0.90992\n",
      "[10]\tvalidation_0-auc:0.91239\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  UserWarning,\n",
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11]\tvalidation_0-auc:0.91302\n",
      "[12]\tvalidation_0-auc:0.91294\n",
      "[13]\tvalidation_0-auc:0.91220\n",
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      "[21]\tvalidation_0-auc:0.91514\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  UserWarning,\n",
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  UserWarning,\n",
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  UserWarning,\n",
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `eval_metric` in `fit` method is deprecated for better compatibility with scikit-learn, use `eval_metric` in constructor or`set_params` instead.\n",
      "  UserWarning,\n",
      "E:\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[794]\tvalidation_0-auc:0.93654\n",
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      "[796]\tvalidation_0-auc:0.93655\n",
      "[797]\tvalidation_0-auc:0.93658\n",
      "[798]\tvalidation_0-auc:0.93657\n",
      "[799]\tvalidation_0-auc:0.93656\n",
      "[800]\tvalidation_0-auc:0.93654\n",
      "[801]\tvalidation_0-auc:0.93653\n",
      "[802]\tvalidation_0-auc:0.93653\n",
      "[803]\tvalidation_0-auc:0.93653\n",
      "[804]\tvalidation_0-auc:0.93652\n",
      "[805]\tvalidation_0-auc:0.93650\n",
      "0.9326219985474677\n"
     ]
    }
   ],
   "source": [
    "n_fold = 5\n",
    "folds = KFold(n_splits=n_fold, shuffle=True, random_state=2022)\n",
    "oof_xgb = np.zeros(len(X))\n",
    "prediction_xgb = np.zeros(len(dummy_test))\n",
    "for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n",
    "    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n",
    "    y_train, y_valid = y[train_index], y[valid_index]\n",
    "#     smote_tomek = SMOTETomek(random_state=2022)\n",
    "#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n",
    "    eval_set = [(X_valid, y_valid)]\n",
    "    model_xgb = XGBRegressor(\n",
    "        max_depth=9,learning_rate=0.01,n_estimators=10000,colsample_bytree=0.6,subsample=0.8,random_state=2022\n",
    "    ).fit(X_train,y_train,early_stopping_rounds=100, eval_metric=\"auc\",eval_set=eval_set, verbose=True)\n",
    "    y_pred_valid = model_xgb.predict(X_valid)\n",
    "    y_pred = model_xgb.predict(dummy_test)\n",
    "    oof_xgb[valid_index] = y_pred_valid.reshape(-1, )\n",
    "    prediction_xgb += y_pred\n",
    "prediction_xgb /= n_fold \n",
    "print(roc_auc_score(y, oof_xgb))\n",
    "# 0.9326219985474677"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D03CB6619D8B44B08E990ED3EA01C61D",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于GridSearchCV的LGBM参数调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "082D0D1C79D14C0BB07870203B74F5F8",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 30, 'n_estimators': 10000, 'num_leaves': 59, 'subsample': 0.7}\n"
     ]
    }
   ],
   "source": [
    "# param = {'max_depth': [30],\n",
    "#          'learning_rate': [0.01],\n",
    "#          'num_leaves': [59],\n",
    "#          'subsample': [0.7],\n",
    "#          'colsample_bytree': [0.8],\n",
    "#          'n_estimators': [10000]}\n",
    "# gs = GridSearchCV(estimator=LGBMRegressor(), param_grid=param, cv=5, scoring=\"neg_mean_squared_error\", n_jobs=-1) \n",
    "# gs.fit(X_resampled,y_resampled)\n",
    "# print(gs.best_params_) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "23ADF0543B77452A8DCC799C0582D474",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于五折交叉验证的LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "D2313DA33FCA4D7EA5F3C32DD98D6BBA",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\ttraining's auc: 0.94085\ttraining's l2: 0.0788925\tvalid_1's auc: 0.925864\tvalid_1's l2: 0.0818997\n",
      "[100]\ttraining's auc: 0.944802\ttraining's l2: 0.0672695\tvalid_1's auc: 0.928742\tvalid_1's l2: 0.0719714\n",
      "[150]\ttraining's auc: 0.948559\ttraining's l2: 0.0607984\tvalid_1's auc: 0.930343\tvalid_1's l2: 0.0672457\n",
      "[200]\ttraining's auc: 0.951549\ttraining's l2: 0.056876\tvalid_1's auc: 0.931374\tvalid_1's l2: 0.0649636\n",
      "[250]\ttraining's auc: 0.954385\ttraining's l2: 0.054275\tvalid_1's auc: 0.931987\tvalid_1's l2: 0.0639735\n",
      "[300]\ttraining's auc: 0.957015\ttraining's l2: 0.0521878\tvalid_1's auc: 0.932396\tvalid_1's l2: 0.0634468\n",
      "[350]\ttraining's auc: 0.960145\ttraining's l2: 0.0503487\tvalid_1's auc: 0.93328\tvalid_1's l2: 0.0630066\n",
      "[400]\ttraining's auc: 0.962937\ttraining's l2: 0.0486323\tvalid_1's auc: 0.93371\tvalid_1's l2: 0.062822\n",
      "[450]\ttraining's auc: 0.965233\ttraining's l2: 0.0471381\tvalid_1's auc: 0.933886\tvalid_1's l2: 0.0627358\n",
      "[500]\ttraining's auc: 0.96724\ttraining's l2: 0.0458021\tvalid_1's auc: 0.934201\tvalid_1's l2: 0.0626506\n",
      "[550]\ttraining's auc: 0.969189\ttraining's l2: 0.0445553\tvalid_1's auc: 0.934529\tvalid_1's l2: 0.0624507\n",
      "[600]\ttraining's auc: 0.970941\ttraining's l2: 0.0434685\tvalid_1's auc: 0.93459\tvalid_1's l2: 0.0623492\n",
      "[650]\ttraining's auc: 0.972602\ttraining's l2: 0.0424998\tvalid_1's auc: 0.934349\tvalid_1's l2: 0.0622475\n",
      "[700]\ttraining's auc: 0.974021\ttraining's l2: 0.041611\tvalid_1's auc: 0.934276\tvalid_1's l2: 0.0621712\n",
      "[750]\ttraining's auc: 0.975347\ttraining's l2: 0.0408109\tvalid_1's auc: 0.933644\tvalid_1's l2: 0.0622134\n",
      "[800]\ttraining's auc: 0.976582\ttraining's l2: 0.0400338\tvalid_1's auc: 0.933249\tvalid_1's l2: 0.0622394\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\ttraining's auc: 0.9402\ttraining's l2: 0.07935\tvalid_1's auc: 0.932536\tvalid_1's l2: 0.0801626\n",
      "[100]\ttraining's auc: 0.943739\ttraining's l2: 0.0676194\tvalid_1's auc: 0.934496\tvalid_1's l2: 0.0702667\n",
      "[150]\ttraining's auc: 0.947848\ttraining's l2: 0.061121\tvalid_1's auc: 0.936416\tvalid_1's l2: 0.065416\n",
      "[200]\ttraining's auc: 0.950776\ttraining's l2: 0.0572456\tvalid_1's auc: 0.937341\tvalid_1's l2: 0.0630831\n",
      "[250]\ttraining's auc: 0.953983\ttraining's l2: 0.0545583\tvalid_1's auc: 0.938133\tvalid_1's l2: 0.0619445\n",
      "[300]\ttraining's auc: 0.956913\ttraining's l2: 0.0523396\tvalid_1's auc: 0.93872\tvalid_1's l2: 0.0612938\n",
      "[350]\ttraining's auc: 0.959894\ttraining's l2: 0.0504202\tvalid_1's auc: 0.939454\tvalid_1's l2: 0.0609051\n",
      "[400]\ttraining's auc: 0.962519\ttraining's l2: 0.0487443\tvalid_1's auc: 0.939785\tvalid_1's l2: 0.0607129\n",
      "[450]\ttraining's auc: 0.964735\ttraining's l2: 0.0473303\tvalid_1's auc: 0.939786\tvalid_1's l2: 0.0606189\n",
      "[500]\ttraining's auc: 0.966875\ttraining's l2: 0.0459614\tvalid_1's auc: 0.940058\tvalid_1's l2: 0.0605379\n",
      "[550]\ttraining's auc: 0.968779\ttraining's l2: 0.0447104\tvalid_1's auc: 0.94022\tvalid_1's l2: 0.0605152\n",
      "[600]\ttraining's auc: 0.97058\ttraining's l2: 0.0435918\tvalid_1's auc: 0.940533\tvalid_1's l2: 0.0604589\n",
      "[650]\ttraining's auc: 0.972085\ttraining's l2: 0.0426417\tvalid_1's auc: 0.940571\tvalid_1's l2: 0.0604517\n",
      "[700]\ttraining's auc: 0.973432\ttraining's l2: 0.0417863\tvalid_1's auc: 0.940672\tvalid_1's l2: 0.0604522\n",
      "[750]\ttraining's auc: 0.974732\ttraining's l2: 0.0409048\tvalid_1's auc: 0.940905\tvalid_1's l2: 0.0603683\n",
      "[800]\ttraining's auc: 0.975899\ttraining's l2: 0.0401361\tvalid_1's auc: 0.940962\tvalid_1's l2: 0.0603816\n",
      "[850]\ttraining's auc: 0.976931\ttraining's l2: 0.0394491\tvalid_1's auc: 0.940966\tvalid_1's l2: 0.0603915\n",
      "[900]\ttraining's auc: 0.977929\ttraining's l2: 0.0387792\tvalid_1's auc: 0.94085\tvalid_1's l2: 0.0604162\n",
      "[950]\ttraining's auc: 0.978862\ttraining's l2: 0.0381312\tvalid_1's auc: 0.940734\tvalid_1's l2: 0.0604662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\ttraining's auc: 0.941654\ttraining's l2: 0.0795005\tvalid_1's auc: 0.922594\tvalid_1's l2: 0.0787988\n",
      "[100]\ttraining's auc: 0.945571\ttraining's l2: 0.0677357\tvalid_1's auc: 0.92527\tvalid_1's l2: 0.0696453\n",
      "[150]\ttraining's auc: 0.949356\ttraining's l2: 0.0611459\tvalid_1's auc: 0.926724\tvalid_1's l2: 0.0655987\n",
      "[200]\ttraining's auc: 0.952608\ttraining's l2: 0.0570597\tvalid_1's auc: 0.928057\tvalid_1's l2: 0.0637265\n",
      "[250]\ttraining's auc: 0.955616\ttraining's l2: 0.0543341\tvalid_1's auc: 0.928873\tvalid_1's l2: 0.0630037\n",
      "[300]\ttraining's auc: 0.95832\ttraining's l2: 0.0522183\tvalid_1's auc: 0.929336\tvalid_1's l2: 0.0626532\n",
      "[350]\ttraining's auc: 0.961151\ttraining's l2: 0.0503588\tvalid_1's auc: 0.930429\tvalid_1's l2: 0.0623272\n",
      "[400]\ttraining's auc: 0.963634\ttraining's l2: 0.0487219\tvalid_1's auc: 0.93069\tvalid_1's l2: 0.0622242\n",
      "[450]\ttraining's auc: 0.965829\ttraining's l2: 0.0472595\tvalid_1's auc: 0.930543\tvalid_1's l2: 0.0621943\n",
      "[500]\ttraining's auc: 0.967929\ttraining's l2: 0.0459291\tvalid_1's auc: 0.930614\tvalid_1's l2: 0.0621045\n",
      "[550]\ttraining's auc: 0.969991\ttraining's l2: 0.0446926\tvalid_1's auc: 0.930791\tvalid_1's l2: 0.0620086\n",
      "[600]\ttraining's auc: 0.971611\ttraining's l2: 0.0436567\tvalid_1's auc: 0.930556\tvalid_1's l2: 0.0619909\n",
      "[650]\ttraining's auc: 0.973089\ttraining's l2: 0.0427368\tvalid_1's auc: 0.930451\tvalid_1's l2: 0.0619711\n",
      "[700]\ttraining's auc: 0.974456\ttraining's l2: 0.0418338\tvalid_1's auc: 0.930732\tvalid_1's l2: 0.0618934\n",
      "[750]\ttraining's auc: 0.975701\ttraining's l2: 0.0409687\tvalid_1's auc: 0.93121\tvalid_1's l2: 0.0617997\n",
      "[800]\ttraining's auc: 0.976774\ttraining's l2: 0.04021\tvalid_1's auc: 0.931229\tvalid_1's l2: 0.0618136\n",
      "[850]\ttraining's auc: 0.977858\ttraining's l2: 0.0394732\tvalid_1's auc: 0.931142\tvalid_1's l2: 0.061852\n",
      "[900]\ttraining's auc: 0.978878\ttraining's l2: 0.0387557\tvalid_1's auc: 0.930934\tvalid_1's l2: 0.0618529\n",
      "[950]\ttraining's auc: 0.979878\ttraining's l2: 0.0380331\tvalid_1's auc: 0.930689\tvalid_1's l2: 0.0618732\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\ttraining's auc: 0.938779\ttraining's l2: 0.0783692\tvalid_1's auc: 0.929724\tvalid_1's l2: 0.0849279\n",
      "[100]\ttraining's auc: 0.943638\ttraining's l2: 0.0667337\tvalid_1's auc: 0.932269\tvalid_1's l2: 0.074493\n",
      "[150]\ttraining's auc: 0.94752\ttraining's l2: 0.0603255\tvalid_1's auc: 0.933807\tvalid_1's l2: 0.0694419\n",
      "[200]\ttraining's auc: 0.950881\ttraining's l2: 0.056408\tvalid_1's auc: 0.93552\tvalid_1's l2: 0.0669414\n",
      "[250]\ttraining's auc: 0.95406\ttraining's l2: 0.0536874\tvalid_1's auc: 0.935899\tvalid_1's l2: 0.0658231\n",
      "[300]\ttraining's auc: 0.957231\ttraining's l2: 0.05144\tvalid_1's auc: 0.936191\tvalid_1's l2: 0.0651672\n",
      "[350]\ttraining's auc: 0.960257\ttraining's l2: 0.0495814\tvalid_1's auc: 0.936883\tvalid_1's l2: 0.064655\n",
      "[400]\ttraining's auc: 0.962809\ttraining's l2: 0.0479463\tvalid_1's auc: 0.937101\tvalid_1's l2: 0.0644035\n",
      "[450]\ttraining's auc: 0.965109\ttraining's l2: 0.0464814\tvalid_1's auc: 0.937094\tvalid_1's l2: 0.0642898\n",
      "[500]\ttraining's auc: 0.967202\ttraining's l2: 0.0451684\tvalid_1's auc: 0.937229\tvalid_1's l2: 0.0641968\n",
      "[550]\ttraining's auc: 0.969275\ttraining's l2: 0.0439568\tvalid_1's auc: 0.937514\tvalid_1's l2: 0.0640906\n",
      "[600]\ttraining's auc: 0.971103\ttraining's l2: 0.0428949\tvalid_1's auc: 0.937774\tvalid_1's l2: 0.0639556\n",
      "[650]\ttraining's auc: 0.972795\ttraining's l2: 0.0419193\tvalid_1's auc: 0.938085\tvalid_1's l2: 0.0638854\n",
      "[700]\ttraining's auc: 0.974376\ttraining's l2: 0.0409977\tvalid_1's auc: 0.938299\tvalid_1's l2: 0.0637705\n",
      "[750]\ttraining's auc: 0.975719\ttraining's l2: 0.0401733\tvalid_1's auc: 0.938254\tvalid_1's l2: 0.0638389\n",
      "[800]\ttraining's auc: 0.976952\ttraining's l2: 0.039378\tvalid_1's auc: 0.938171\tvalid_1's l2: 0.0638989\n",
      "[850]\ttraining's auc: 0.977996\ttraining's l2: 0.0386485\tvalid_1's auc: 0.93817\tvalid_1's l2: 0.0639324\n",
      "[900]\ttraining's auc: 0.978923\ttraining's l2: 0.0380388\tvalid_1's auc: 0.938106\tvalid_1's l2: 0.0639588\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50]\ttraining's auc: 0.941106\ttraining's l2: 0.0789061\tvalid_1's auc: 0.916192\tvalid_1's l2: 0.0814378\n",
      "[100]\ttraining's auc: 0.945514\ttraining's l2: 0.0669373\tvalid_1's auc: 0.919579\tvalid_1's l2: 0.0723827\n",
      "[150]\ttraining's auc: 0.949487\ttraining's l2: 0.0604246\tvalid_1's auc: 0.921699\tvalid_1's l2: 0.068301\n",
      "[200]\ttraining's auc: 0.953036\ttraining's l2: 0.0563975\tvalid_1's auc: 0.922555\tvalid_1's l2: 0.0664568\n",
      "[250]\ttraining's auc: 0.955869\ttraining's l2: 0.0536905\tvalid_1's auc: 0.923021\tvalid_1's l2: 0.0656804\n",
      "[300]\ttraining's auc: 0.958501\ttraining's l2: 0.0515648\tvalid_1's auc: 0.923196\tvalid_1's l2: 0.0654114\n",
      "[350]\ttraining's auc: 0.961319\ttraining's l2: 0.0497304\tvalid_1's auc: 0.924204\tvalid_1's l2: 0.0651377\n",
      "[400]\ttraining's auc: 0.963981\ttraining's l2: 0.0480409\tvalid_1's auc: 0.924832\tvalid_1's l2: 0.0648762\n",
      "[450]\ttraining's auc: 0.96626\ttraining's l2: 0.0466098\tvalid_1's auc: 0.925281\tvalid_1's l2: 0.0647658\n",
      "[500]\ttraining's auc: 0.968523\ttraining's l2: 0.0452797\tvalid_1's auc: 0.925673\tvalid_1's l2: 0.0647242\n",
      "[550]\ttraining's auc: 0.970423\ttraining's l2: 0.0440425\tvalid_1's auc: 0.926443\tvalid_1's l2: 0.0646256\n",
      "[600]\ttraining's auc: 0.972048\ttraining's l2: 0.0429566\tvalid_1's auc: 0.926666\tvalid_1's l2: 0.064639\n",
      "[650]\ttraining's auc: 0.97356\ttraining's l2: 0.0419683\tvalid_1's auc: 0.926761\tvalid_1's l2: 0.0646741\n",
      "[700]\ttraining's auc: 0.974844\ttraining's l2: 0.0411034\tvalid_1's auc: 0.926648\tvalid_1's l2: 0.0647594\n",
      "[750]\ttraining's auc: 0.976116\ttraining's l2: 0.0402927\tvalid_1's auc: 0.92654\tvalid_1's l2: 0.0648155\n",
      "0.9342991211145983\n"
     ]
    }
   ],
   "source": [
    "n_fold = 5\n",
    "folds = KFold(n_splits=n_fold, shuffle=True,random_state=1314)\n",
    "params = {\n",
    "    'learning_rate':0.01,\n",
    "    'subsample': 0.7,\n",
    "    'num_leaves': 59,\n",
    "    'n_estimators':1500,\n",
    "    'max_depth': 30,\n",
    "    'colsample_bytree': 0.8,\n",
    "    'verbose': -1,\n",
    "    'seed': 2022,\n",
    "    'n_jobs': -1\n",
    "}\n",
    "\n",
    "oof_lgb = np.zeros(len(X))\n",
    "predictions_lgb  = np.zeros(len(dummy_test))\n",
    "for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n",
    "    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n",
    "    y_train, y_valid = y[train_index], y[valid_index]\n",
    "#     smote_tomek = SMOTETomek(random_state=2022)\n",
    "#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n",
    "    model = lgb.LGBMRegressor(**params)\n",
    "    model.fit(X_train, y_train,\n",
    "              eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
    "              eval_metric='auc',\n",
    "              verbose=50, early_stopping_rounds=200)\n",
    "    y_pred_valid = model.predict(X_valid)\n",
    "    y_pred = model.predict(dummy_test, num_iteration=model.best_iteration_)\n",
    "    oof_lgb[valid_index] = y_pred_valid.reshape(-1, )\n",
    "    predictions_lgb  += y_pred\n",
    "predictions_lgb  /= n_fold\n",
    "print(roc_auc_score(y, oof_lgb))\n",
    "# 0.9342991211145983"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1D0CF99ED0F04505ADAF99D733B04257",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于GridSearchCV的catboost参数调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "id": "40143CE0A1F7461A9D5AAFEB614609B5",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# param = {'depth': [7,9,11],\n",
    "#          'learning_rate': [0.01],\n",
    "#          'iterations':  [8000]}\n",
    "# gs = GridSearchCV(estimator=CatBoostRegressor(), param_grid=param, cv=3, scoring=\"neg_mean_squared_error\", n_jobs=-1) \n",
    "# gs.fit(X_resampled,y_resampled)\n",
    "# print(gs.best_params_) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0E595CF0D95341E69AB1154B1849F14E",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于五折交叉验证的catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "3A557EC583C0481488702DC367D10F53",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.3201355\ttest: 0.3215319\tbest: 0.3215319 (0)\ttotal: 55.1ms\tremaining: 22m 57s\n",
      "1000:\tlearn: 0.2210492\ttest: 0.2482059\tbest: 0.2482059 (1000)\ttotal: 34.7s\tremaining: 13m 51s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.2468782072\n",
      "bestIteration = 1477\n",
      "\n",
      "Shrink model to first 1478 iterations.\n",
      "0:\tlearn: 0.3208935\ttest: 0.3185105\tbest: 0.3185105 (0)\ttotal: 69.5ms\tremaining: 28m 57s\n",
      "1000:\tlearn: 0.2206024\ttest: 0.2464808\tbest: 0.2464751 (998)\ttotal: 37.8s\tremaining: 15m 5s\n",
      "2000:\tlearn: 0.1971871\ttest: 0.2454924\tbest: 0.2454188 (1725)\ttotal: 1m 13s\tremaining: 14m 5s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.2454188002\n",
      "bestIteration = 1725\n",
      "\n",
      "Shrink model to first 1726 iterations.\n",
      "0:\tlearn: 0.3214860\ttest: 0.3161403\tbest: 0.3161403 (0)\ttotal: 42ms\tremaining: 17m 30s\n",
      "1000:\tlearn: 0.2210140\ttest: 0.2480678\tbest: 0.2480521 (995)\ttotal: 36.2s\tremaining: 14m 27s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.24699407\n",
      "bestIteration = 1556\n",
      "\n",
      "Shrink model to first 1557 iterations.\n",
      "0:\tlearn: 0.3186885\ttest: 0.3274174\tbest: 0.3274174 (0)\ttotal: 54ms\tremaining: 22m 30s\n",
      "1000:\tlearn: 0.2202664\ttest: 0.2525361\tbest: 0.2525361 (1000)\ttotal: 36.4s\tremaining: 14m 32s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.2515410448\n",
      "bestIteration = 1603\n",
      "\n",
      "Shrink model to first 1604 iterations.\n",
      "0:\tlearn: 0.3209030\ttest: 0.3185929\tbest: 0.3185929 (0)\ttotal: 75.6ms\tremaining: 31m 29s\n",
      "1000:\tlearn: 0.2212537\ttest: 0.2523067\tbest: 0.2523067 (1000)\ttotal: 34.7s\tremaining: 13m 51s\n",
      "Stopped by overfitting detector  (300 iterations wait)\n",
      "\n",
      "bestTest = 0.250998901\n",
      "bestIteration = 1618\n",
      "\n",
      "Shrink model to first 1619 iterations.\n"
     ]
    }
   ],
   "source": [
    "# 本地交叉验证\n",
    "n_fold = 5\n",
    "folds = KFold(n_splits=n_fold, shuffle=True, random_state=1314)\n",
    "\n",
    "oof_cat = np.zeros(len(X))\n",
    "prediction_cat = np.zeros(len(dummy_test))\n",
    "for fold_n, (train_index, valid_index) in enumerate(folds.split(X)):\n",
    "    X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]\n",
    "    y_train, y_valid = y[train_index], y[valid_index]\n",
    "#     smote_tomek = SMOTETomek(random_state=2022)\n",
    "#     X_resampled, y_resampled = smote_tomek.fit_resample(X_train, y_train)\n",
    "    train_pool = Pool(X_train, y_train)\n",
    "    eval_pool = Pool(X_valid, y_valid)\n",
    "    cbt_model = CatBoostRegressor(iterations=25000, # 注：baseline 提到的分数是用 iterations=60000 得到的，但运行时间有点久\n",
    "                           learning_rate=0.01, # 注：事实上好几个 property 在 lr=0.1 时收敛巨慢。后面可以考虑调大\n",
    "#                            eval_metric='SMAPE',\n",
    "                                  depth=9,\n",
    "                           use_best_model=True,\n",
    "                           random_seed=2022,\n",
    "                           logging_level='Verbose',\n",
    "                           #task_type='GPU',\n",
    "                           devices='0',\n",
    "                           gpu_ram_part=0.5,\n",
    "                           early_stopping_rounds=300)\n",
    "    \n",
    "    cbt_model.fit(train_pool,\n",
    "              eval_set=eval_pool,\n",
    "              verbose=1000)\n",
    "\n",
    "    y_pred_valid = cbt_model.predict(X_valid)\n",
    "    y_pred_c = cbt_model.predict(dummy_test)\n",
    "    oof_cat[valid_index] = y_pred_valid.reshape(-1, )\n",
    "    prediction_cat += y_pred_c\n",
    "prediction_cat /= n_fold \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "27A63B61DB094FCF95CEEB6A1E3ADC5A",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.935264298588153\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y, oof_cat))\n",
    "# 0.935264298588153"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9470A77D276E418F9CB2ACD8D18E6F68",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "### 基于stacking的模型融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "4A24F209D61E47E7A27F4A26D6687B89",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0\n",
      "fold 1\n",
      "fold 2\n",
      "fold 3\n",
      "fold 4\n",
      "fold 5\n",
      "fold 6\n",
      "fold 7\n",
      "fold 8\n",
      "fold 9\n",
      "CV score: 0.06156606\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.linear_model import Bayesian\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,make_scorer\n",
    "\n",
    "# 将多个模型的结果进行stacking（叠加）\n",
    "train_stack = np.vstack([oof_rf,oof_lgb,oof_cat,oof_xgb]).transpose()\n",
    "test_stack = np.vstack([prediction_rf,prediction_lgb,prediction_cat,prediction_xgb]).transpose()\n",
    "#贝叶斯分类器也使用交叉验证的方法，5折，重复2次\n",
    "folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=2018)\n",
    "oof_stack = np.zeros(train_stack.shape[0])\n",
    "predictions = np.zeros(test_stack.shape[0])\n",
    " \n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack,y)):\n",
    "    print(\"fold {}\".format(fold_))\n",
    "    trn_data, trn_y = train_stack[trn_idx], y.iloc[trn_idx].values\n",
    "    val_data, val_y = train_stack[val_idx], y.iloc[val_idx].values#\n",
    "    \n",
    "    clf_3 = BayesianRidge()\n",
    "    clf_3.fit(trn_data, trn_y)\n",
    "    \n",
    "    oof_stack[val_idx] = clf_3.predict(val_data)#对验证集有一个预测，用于后面计算模型的偏差\n",
    "    predictions += clf_3.predict(test_stack) / 10#对测试集的预测，除以10是因为5折交叉验证重复了2次\n",
    "    \n",
    "mean_squared_error(y.values, oof_stack)#计算出模型在训练集上的均方误差\n",
    "print(\"CV score: {:<8.8f}\".format(mean_squared_error(y.values, oof_stack)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "33AB2F850236462DB7F0DFE549A2C559",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9361018703876826\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y, oof_stack))\n",
    "# 0.9361018703876826"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1D7C809913E14DD79D1E399F519EDC1C",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "source": [
    "# 保存结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "id": "BC37DA49289D45C2951D2FFDC91043BA",
    "jupyter": {},
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [],
    "trusted": true
   },
   "outputs": [],
   "source": [
    "test['pred'] = predictions\n",
    "test[['ID', 'pred']].to_csv(r'C:\\Users\\hepei\\Desktop\\比赛代码\\练习赛\\客户购买预测\\结果\\sub.csv', index=None, encoding=\"utf-8\")"
   ]
  }
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